Evaluation metrics.
Following the previous novel view synthesis works [1]}, [2]}, [3]}, we use PSNR, SSIM, and LPIPS [4]} as metrics to evaluate the synthesis. However, one key difference between our work and previous ones is that our setup is a learning setup (with training and validation), while previous works treat it as an optimization problem. Most previous works on novel view synthesis try to only generalize the generated views on the same shape, while we aim to generalize across shapes and across views. We treat the collected whole SRFs as ground truth labels for the input few images from the training set. We consider the validation PSNR, SSIM, and LPIPS of the input images at the validation SRF set of shapes (on the test images of those shapes) as the main evaluation metrics. Also, we report validation accuracy = \(\frac{\text{validation PSNR with test few images}}{\text{whole SRF optimzation's PSNR}}\)  and propose it as a new metric to evaluate such a learning setup of SRFs. Furthermore, as we describe in Section , SPARF has 10 posed Out-Of-Distribution (OOD) images for every 3D shape to evaluate the robustness of novel view synthesis methods in the unconstrained setup. We report these OOD PSNR, SSIM, LPIPS, and Accuracy as well.
